Modelling and performance of an active heave compensator for offshore operations

The development of offshore oil and gas fields require the placement of different equipment on the sea floor. This is done by deploying the equipment from vessels operating in dynamic positioning on the surface. The deployment operation has different phases, and in higher sea states, it may require wave-load synchronization, when the load is going through the splash zone, and heave compensation when the load is close to the sea floor. In this paper, we analyse the performance of a particular type of hardware operating in a heave compensation mode. We derive a comprehensive model, analyse limits of performance and evaluate a control strategy.

Highly sensitive electrochemical sensor for nitric oxide using the self-assembled monolayer of 1,8,15,22-tetraaminophthalo-cyanatocobalt(II) on glassy carbon electrode

Glassy carbon (GC) electrode modified with a self-assembled monolayer (SAM) of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) was used for the selective and highly sensitive determination of nitric oxide (NO). The SAM of 4α-CoIITAPc was formed on GC electrode by spontaneous adsorption from DMF containing 1 mM 4α-CoIITAPc. The SAM showed two pairs of well-defined redox peaks corresponding to CoIII/CoII and CoIIIPc−1/CoIIIPc−2 in 0.2 M phosphate buffer (PB) solution (pH 2.5). The SAM modified electrode showed excellent electrocatalytic activity towards the oxidation of nitric oxide (NO) by enhancing its oxidation current with 310 mV less positive potential shift when compared to bare GC electrode. In amperometric measurements, the current response for NO oxidation was linearly increased in the concentration range of 3×10−9 to 30×10−9 M with a detection limit of 1.4×10−10 M (S/N=3). The proposed method showed a better recovery for NO in human blood serum samples.

Selective electrochemical epinephrine sensor using self-assembled monomolecular film of 1,8,15,22-tetraaminophthalocyanatonickel(II) on gold electrode

This article describes the highly sensitive and selective determination of epinephrine (EP) using self-assembled monomolecular film (SAMF) of 1,8,15,22-tetraamino-phthalocyanatonickel(II) (4α-NiIITAPc) on Au electrode. The 4α-NiIITAPc SAMF modified electrode was prepared by spontaneous adsorption of 4α-NiIITAPc from dimethylformamide solution. The modified electrode oxidizes EP at less over potential with enhanced current response in contrast to the bare Au electrode. The standard heterogeneous rate constant (k°) for the oxidation of EP at 4α-NiIITAPc SAMF modified electrode was found to be 1.94×10−2 cm s−1 which was much higher than that at the bare Au electrode. Further, it was found that 4α-NiIITAPc SAMF modified electrode separates the voltammetric signals of ascorbic acid (AA) and EP with a peak separation of 250 mV. Using amperometric method the lowest detection limit of 50 nM of EP was achieved at SAMF modified electrode. Simultaneous amperometric determination of AA and EP was also achieved at the SAMF modified electrode. Common physiological interferents such as uric acid, glucose, urea and NaCl do not interfere within the potential window of EP oxidation. The present 4α-NiIITAPc SAMF modified electrode was also successfully applied to determine the concentration of EP in commercially available injection.

Fourier active appearance models

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

Practical analysis of big acoustic sensor data for environmental monitoring

Monitoring the environment with acoustic sensors is an effective method for understanding changes in ecosystems. Through extensive monitoring, large-scale, ecologically relevant, datasets can be produced that can inform environmental policy. The collection of acoustic sensor data is a solved problem; the current challenge is the management and analysis of raw audio data to produce useful datasets for ecologists. This paper presents the applied research we use to analyze big acoustic datasets. Its core contribution is the presentation of practical large-scale acoustic data analysis methodologies. We describe details of the data workflows we use to provide both citizen scientists and researchers practical access to large volumes of ecoacoustic data. Finally, we propose a work in progress large-scale architecture for analysis driven by a hybrid cloud-and-local production-grade website.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

An SECM study on the influence of cationic, membrane-active peptides on a gold-supported self-assembled monolayer

The influence of the membrane active peptides, Tat44–57 (activator in HIV-1) and melittin (active content of bee venom), on self-assembled monolayers of 6-mercaptohexanoic acid (MHA) on gold electrodes has been studied with scanning electrochemical microscopy (SECM). It was found that MHA, when deprotonated at physiological pH, significantly affected the relative rates of electron transfer between the [Fe(CN)6]4− solution based mediator and the underlying gold electrode, predominantly by the electrostatic interaction between the mediator and MHA. Upon the introduction of Tat44–57 ormelittin to the electrolyte, the relative rate of electron transfer through the MHA layer could be increased or decreased depending on the mediator used. However, in all cases it was found that these peptides have the ability to be incorporated into synthetic SAMs, which has implications for future electrochemical studies carried out using cell mimicking membranes immobilised on such layers.

Prevalence of active foot disease and foot disease risk factors in a subacute inpatient rehabilitation facility: a cross-sectional prevalence study

Background Australian subacute inpatient rehabilitation facilities face significant challenges from the ageing population and the increasing burden of chronic disease. Foot disease complications are a negative consequence of many chronic diseases. With the rapid expansion of subacute rehabilitation inpatient services, it seems imperative to investigate the prevalence of foot disease and foot disease risk factors in this population. The primary aim of this cross-sectional study was to determine the prevalence of active foot disease and foot disease risk factors in a subacute inpatient rehabilitation facility. Methods Eligible participants were all adults admitted at least overnight into a large Australian subacute inpatient rehabilitation facility over two different four week periods. Consenting participants underwent a short non-invasive foot examination by a podiatrist utilising the validated Queensland Health High Risk Foot Form to collect data on age, sex, medical co-morbidity history, foot disease risk factor history and clinically diagnosed foot disease complications and foot disease risk factors. Descriptive statistics were used to determine the prevalence of clinically diagnosed foot disease complications, foot disease risk factors and groups of foot disease risk factors. Logistic regression analyses were used to investigate any associations between defined explanatory variables and appropriate foot disease outcome variables. Results Overall, 85 (88%) of 97 people admitted to the facility during the study periods consented; mean age 80 (±9) years and 71% were female. The prevalence (95% confidence interval) of participants with active foot disease was 11.8% (6.3 – 20.5), 32.9% (23.9 – 43.5) had multiple foot disease risk factors, and overall, 56.5% (45.9 – 66.5) had at least one foot disease risk factor. A self-reported history of peripheral neuropathy diagnosis was independently associated with having multiple foot disease risk factors (OR 13.504, p = 0.001). Conclusion This study highlights the potential significance of the burden of foot disease in subacute inpatient rehabilitation facilities. One in eight subacute inpatients were admitted with active foot disease and one in two with at least one foot disease risk factor in this study. It is recommended that further multi-site studies and management guidelines are required to address the foot disease burden in subacute inpatient rehabilitation facilities. Keywords: Subacute; Inpatient; Foot; Complication; Prevalence

Modeling kinect sensor noise for improved 3D reconstruction and tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy. © 2012 IEEE.

Modeling and control of a CLC resonant dual active bridge

This paper proposes a linear large signal state-space model for a phase controlled CLC (Capacitor Inductor Capacitor) Resonant Dual Active Bridge (RDAB). The proposed model is useful for fast simulation and for the estimation of state variables under large signal variation. The model is also useful for control design because the slow changing dynamics of the dq variables are relatively easy to control. Simulation results of the proposed model are presented and compared to the simulated circuit model to demonstrate the proposed model's accuracy. This proposed model was used for the design of a Proportional-Integral (PI) controller and it has been implemented in the circuit simulation to show the proposed models usefulness in control design.